Move various functions from main matlab folder to subfolders

license.txt

@@ -113,7 +113,7 @@ Copyright: 1995 E.G.Tsionas
            2015-2017 Dynare Team
 License: GPL-3+
 
-Files: matlab/endogenous_prior.m
+Files: matlab/estimation/endogenous_prior.m
 Copyright: 2011 Lawrence J. Christiano, Mathias Trabandt and Karl Walentin
            2013-2017 Dynare Team
 License: GPL-3+
@@ -128,7 +128,7 @@ Copyright: 2016 Benjamin Born and Johannes Pfeifer
            2016-2017 Dynare Team
 License: GPL-3+
 
-Files: matlab/commutation.m matlab/duplication.m
+Files: matlab/+pruned_SS/commutation.m matlab/+pruned_SS/duplication.m
 Copyright: 1997 Tom Minka <minka@microsoft.com>
            2019-2020 Dynare Team
 License: GPL-3+
@@ -141,7 +141,7 @@ Comment: The original author gave authorization to change
  the license from BSD-2-clause to GPL-3+ and redistribute 
  it under GPL-3+ with Dynare.
 
-Files: matlab/uperm.m
+Files: matlab/+pruned_SS/uperm.m
 Copyright: 2014 Bruno Luong <brunoluong@yahoo.com> 
            2020 Dynare Team
 License: GPL-3+
@@ -149,7 +149,7 @@ Comment: The original author gave authorization to change
  the license from BSD-2-clause to GPL-3+ and redistribute 
  it under GPL-3+ with Dynare.
 
-Files: matlab/prodmom.m matlab/bivmom.m
+Files: matlab/+pruned_SS/prodmom.m matlab/+pruned_SS/bivmom.m
 Copyright: 2008-2015 Raymond Kan <kan@chass.utoronto.ca>
            2019-2020 Dynare Team
 License: GPL-3+

matlab/+identification/analysis.m

@@ -375,7 +375,7 @@ if info(1) == 0 %no errors in solution
             if size(quant,1)==1
                 si_dMOMENTSnorm = abs(quant).*normaliz_prior_std;
             else
-                si_dMOMENTSnorm = vnorm(quant).*normaliz_prior_std;
+                si_dMOMENTSnorm = identification.vnorm(quant).*normaliz_prior_std;
             end
             iy = find(diag_chh);
             ind_dREDUCEDFORM = ind_dREDUCEDFORM(iy);
@@ -385,7 +385,7 @@ if info(1) == 0 %no errors in solution
                 if size(quant,1)==1
                     si_dREDUCEDFORMnorm = abs(quant).*normaliz_prior_std;
                 else
-                    si_dREDUCEDFORMnorm = vnorm(quant).*normaliz_prior_std;
+                    si_dREDUCEDFORMnorm = identification.vnorm(quant).*normaliz_prior_std;
                 end
             else
                 si_dREDUCEDFORMnorm = [];
@@ -399,7 +399,7 @@ if info(1) == 0 %no errors in solution
                 if size(quant,1)==1
                     si_dDYNAMICnorm = abs(quant).*normaliz_prior_std(stderrparam_nbr+corrparam_nbr+1:end);
                 else
-                    si_dDYNAMICnorm = vnorm(quant).*normaliz_prior_std(stderrparam_nbr+corrparam_nbr+1:end);
+                    si_dDYNAMICnorm = identification.vnorm(quant).*normaliz_prior_std(stderrparam_nbr+corrparam_nbr+1:end);
                 end
             else
                 si_dDYNAMICnorm=[];

matlab/+identification/checks.m

@@ -75,7 +75,7 @@ end
 
 % find non-zero columns at machine precision
 if size(Xpar,1) > 1
-    ind1 = find(vnorm(Xpar) >= eps);
+    ind1 = find(identification.vnorm(Xpar) >= eps);
 else
     ind1 = find(abs(Xpar) >= eps);   % if only one parameter
 end

matlab/+identification/fjaco.m

@@ -30,7 +30,7 @@ function fjac = fjaco(f,x,varargin)
 ff=feval(f,x,varargin{:});
 
 tol = eps.^(1/3); %some default value
-if strcmp(func2str(f),'get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective')
+if strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective')
     tol= varargin{4}.dynatol.x;
 end
 h = tol.*max(abs(x),1);
@@ -40,12 +40,12 @@ fjac = NaN(length(ff),length(x));
 for j=1:length(x)
     xx = x;
     xx(j) = xh1(j); f1=feval(f,xx,varargin{:});
-    if isempty(f1) && (strcmp(func2str(f),'get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
+    if isempty(f1) && (strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
         [~,info]=feval(f,xx,varargin{:});
         disp_info_error_identification_perturbation(info,j);
     end
     xx(j) = xh0(j); f0=feval(f,xx,varargin{:});
-    if isempty(f0) && (strcmp(func2str(f),'get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
+    if isempty(f0) && (strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
         [~,info]=feval(f,xx,varargin{:});
         disp_info_error_identification_perturbation(info,j)
     end

matlab/+identification/get_jacobians.m

@@ -153,7 +153,7 @@ obs_nbr         = length(indvobs);
 d2flag          = 0; % do not compute second parameter derivatives
 
 % Get Jacobians (wrt selected params) of steady state, dynamic model derivatives and perturbation solution matrices for all endogenous variables
-dr.derivs = get_perturbation_params_derivs(M_, options_, estim_params, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indpmodel, indpstderr, indpcorr, d2flag);
+dr.derivs = identification.get_perturbation_params_derivs(M_, options_, estim_params, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indpmodel, indpstderr, indpcorr, d2flag);
 
 [I,~] = find(lead_lag_incidence'); %I is used to select nonzero columns of the Jacobian of endogenous variables in dynamic model files
 yy0 = dr.ys(I);           %steady state of dynamic (endogenous and auxiliary variables) in lead_lag_incidence order
@@ -230,7 +230,7 @@ elseif order == 3
 end
 
 % Get (pruned) state space representation:
-pruned = pruned_state_space_system(M_, options_, dr, indvobs, nlags, useautocorr, 1);
+pruned = pruned_SS.pruned_state_space_system(M_, options_, dr, indvobs, nlags, useautocorr, 1);
 MEAN  = pruned.E_y;
 dMEAN = pruned.dE_y;
 %storage for Jacobians used in dsge_likelihood.m for analytical Gradient and Hession of likelihood (only at order=1)
@@ -258,7 +258,7 @@ if ~no_identification_moments
     
     if kronflag == -1
         %numerical derivative of autocovariogram
-        dMOMENTS = fjaco(str2func('identification.numerical_objective'), xparam1, 1, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=1]
+        dMOMENTS = identification.fjaco(str2func('identification.numerical_objective'), xparam1, 1, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=1]
         dMOMENTS = [dMEAN; dMOMENTS]; %add Jacobian of steady state of VAROBS variables
     else
         dMOMENTS = zeros(obs_nbr + obs_nbr*(obs_nbr+1)/2 + nlags*obs_nbr^2 , totparam_nbr);
@@ -315,7 +315,7 @@ if ~no_identification_spectrum
     IA = eye(size(pruned.A,1));
     if kronflag == -1
         %numerical derivative of spectral density
-        dOmega_tmp = fjaco(str2func('identification.numerical_objective'), xparam1, 2, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=2]
+        dOmega_tmp = identification.fjaco(str2func('identification.numerical_objective'), xparam1, 2, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=2]
         kk = 0;
         for ig = 1:length(freqs)
             kk = kk+1;
@@ -333,7 +333,7 @@ if ~no_identification_spectrum
         dC = reshape(pruned.dC,size(pruned.dC,1)*size(pruned.dC,2),size(pruned.dC,3));
         dD = reshape(pruned.dD,size(pruned.dD,1)*size(pruned.dD,2),size(pruned.dD,3));
         dVarinov = reshape(pruned.dVarinov,size(pruned.dVarinov,1)*size(pruned.dVarinov,2),size(pruned.dVarinov,3));
-        K_obs_exo = commutation(obs_nbr,size(pruned.Varinov,1));
+        K_obs_exo = pruned_SS.commutation(obs_nbr,size(pruned.Varinov,1));
         for ig=1:length(freqs)
             z = tneg(ig);
             zIminusA =  (z*IA - pruned.A);
@@ -400,7 +400,7 @@ if ~no_identification_minimal
         SYS.dC = dr.derivs.dghx(pruned.indy,:,:);
         SYS.D  = dr.ghu(pruned.indy,:);
         SYS.dD = dr.derivs.dghu(pruned.indy,:,:);
-        [CheckCO,minnx,SYS] = get_minimal_state_representation(SYS,1);
+        [CheckCO,minnx,SYS] = identification.get_minimal_state_representation(SYS,1);
         
         if CheckCO == 0
             warning_KomunjerNg = 'WARNING: Komunjer and Ng (2011) failed:\n';
@@ -423,7 +423,7 @@ if ~no_identification_minimal
             dvechSig = dvechSig(indvechSig,:);
             Inx = eye(minnx);
             Inu = eye(exo_nbr);
-            [~,Enu] = duplication(exo_nbr);
+            [~,Enu] = pruned_SS.duplication(exo_nbr);
             KomunjerNg_DL = [dminA; dminB; dminC; dminD; dvechSig];
             KomunjerNg_DT = [kron(transpose(minA),Inx) - kron(Inx,minA);
                              kron(transpose(minB),Inx);

matlab/+identification/get_perturbation_params_derivs.m

@@ -191,7 +191,7 @@ if order > 1 && analytic_derivation_mode == 1
     analytic_derivation_mode = 0; fprintf('As order > 1, reset ''analytic_derivation_mode'' to 0\n');
 end
 
-numerical_objective_fname = str2func('get_perturbation_params_derivs_numerical_objective');
+numerical_objective_fname = str2func('identification.get_perturbation_params_derivs_numerical_objective');
 idx_states      = nstatic+(1:nspred); %index for state variables, in DR order
 modparam_nbr    = length(indpmodel);  %number of selected model parameters
 stderrparam_nbr = length(indpstderr); %number of selected stderr parameters
@@ -295,7 +295,7 @@ if analytic_derivation_mode == -1
 % - perturbation solution matrices: dghx, dghu, dghxx, dghxu, dghuu, dghs2, dghxxx, dghxxu, dghxuu, dghuuu, dghxss, dghuss
 
     %Parameter Jacobian of covariance matrix and solution matrices (wrt selected stderr, corr and model paramters)
-    dSig_gh         = fjaco(numerical_objective_fname, xparam1, 'perturbation_solution', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
+    dSig_gh         = identification.fjaco(numerical_objective_fname, xparam1, 'perturbation_solution', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
     ind_Sigma_e     = (1:exo_nbr^2);
     ind_ghx         = ind_Sigma_e(end) + (1:endo_nbr*nspred);
     ind_ghu         = ind_ghx(end) + (1:endo_nbr*exo_nbr);
@@ -348,7 +348,7 @@ if analytic_derivation_mode == -1
     end
 
     %Parameter Jacobian of dynamic model derivatives (wrt selected model parameters only)
-    dYss_g = fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
+    dYss_g = identification.fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
     ind_Yss = 1:endo_nbr;
     if options_.discretionary_policy || options_.ramsey_policy
         ind_g1 = ind_Yss(end) + (1:M_.eq_nbr*yy0ex0_nbr);
@@ -374,7 +374,7 @@ if analytic_derivation_mode == -1
         % Hessian (wrt paramters) of steady state and first-order solution matrices ghx and Om
         % note that hessian_sparse.m (contrary to hessian.m) does not take symmetry into account, but focuses already on unique values
         options_.order = 1; %make sure only first order
-        d2Yss_KalmanA_Om = hessian_sparse(numerical_objective_fname, xparam1, gstep, 'Kalman_Transition', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
+        d2Yss_KalmanA_Om = identification.hessian_sparse(numerical_objective_fname, xparam1, gstep, 'Kalman_Transition', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
         options_.order = order; %make sure to set back
         ind_KalmanA = ind_Yss(end) + (1:endo_nbr^2);
         DERIVS.d2KalmanA = d2Yss_KalmanA_Om(ind_KalmanA, indp2tottot2);               %only unique elements
@@ -394,7 +394,7 @@ if analytic_derivation_mode == -2
 % The parameter derivatives of perturbation solution matrices are computed analytically below (analytic_derivation_mode=0)
     if order == 3
         [~, g1, g2, g3] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1);
-        g3 = unfold_g3(g3, yy0ex0_nbr);
+        g3 = identification.unfold_g3(g3, yy0ex0_nbr);
     elseif order == 2
         [~, g1, g2] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1);
     elseif order == 1
@@ -405,7 +405,7 @@ if analytic_derivation_mode == -2
         % computation of d2Yss and d2g1
         % note that hessian_sparse does not take symmetry into account, i.e. compare hessian_sparse.m to hessian.m, but focuses already on unique values, which are duplicated below
         options_.order = 1; %d2flag requires only first order
-        d2Yss_g1 = hessian_sparse(numerical_objective_fname, modparam1, gstep, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);  % d2flag requires only first-order
+        d2Yss_g1 = identification.hessian_sparse(numerical_objective_fname, modparam1, gstep, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);  % d2flag requires only first-order
         options_.order = order; %make sure to set back the order
         d2Yss = reshape(full(d2Yss_g1(1:endo_nbr,:)), [endo_nbr modparam_nbr modparam_nbr]); %put into tensor notation
         for j=1:endo_nbr
@@ -431,7 +431,7 @@ if analytic_derivation_mode == -2
     end
 
     %Parameter Jacobian of dynamic model derivatives (wrt selected model parameters only)
-    dYss_g  = fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
+    dYss_g  = identification.fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
     ind_Yss = 1:endo_nbr;
     ind_g1  = ind_Yss(end) + (1:endo_nbr*yy0ex0_nbr);
     dYss    = dYss_g(ind_Yss,:); %in tensor notation, wrt selected model parameters only
@@ -460,7 +460,7 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
         [~, ~, g2_static] = feval([fname,'.static'], ys, exo_steady_state', params); %g2_static is [endo_nbr by endo_nbr^2] second derivative (wrt all endogenous variables) of static model equations f, i.e. d(df/dys)/dys, in declaration order
         if order < 3
             [~, g1, g2, g3] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1); %note that g3 does not contain symmetric elements
-            g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
+            g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
         else
             T  = NaN(sum(dynamic_tmp_nbr(1:5)));
             T  = feval([fname, '.dynamic_g4_tt'], T, ys(I), exo_steady_state', params, ys, 1);
@@ -468,8 +468,8 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
             g2 = feval([fname, '.dynamic_g2'],    T, ys(I), exo_steady_state', params, ys, 1, false); %g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
             g3 = feval([fname, '.dynamic_g3'],    T, ys(I), exo_steady_state', params, ys, 1, false); %note that g3 does not contain symmetric elements
             g4 = feval([fname, '.dynamic_g4'],    T, ys(I), exo_steady_state', params, ys, 1, false); %note that g4 does not contain symmetric elements
-            g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
-            g4 = unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
+            g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
+            g4 = identification.unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
         end
         %g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
         %g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
@@ -556,7 +556,7 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
                 error('For analytical parameter derivatives ''dynamic_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
             end
             [~, g1, g2, g3] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1); %note that g3 does not contain symmetric elements
-            g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
+            g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
                 %g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
                 %g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
                 %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
@@ -575,8 +575,8 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
             g2 = feval([fname, '.dynamic_g2'],    T, ys(I), exo_steady_state', params, ys, 1, false); %g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
             g3 = feval([fname, '.dynamic_g3'],    T, ys(I), exo_steady_state', params, ys, 1, false); %note that g3 does not contain symmetric elements
             g4 = feval([fname, '.dynamic_g4'],    T, ys(I), exo_steady_state', params, ys, 1, false); %note that g4 does not contain symmetric elements
-            g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
-            g4 = unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
+            g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
+            g4 = identification.unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
         end
     end
     % Parameter Jacobian of steady state in different orderings, note dys is in declaration order
@@ -801,7 +801,7 @@ if analytic_derivation_mode == 1
     dghu = [zeros(endo_nbr*exo_nbr, stderrparam_nbr+corrparam_nbr) dghu];
 
     % Compute dOm = dvec(ghu*Sigma_e*ghu') from expressions 34 in Iskrev (2010) Appendix A
-    dOm = kron(I_endo,ghu*Sigma_e)*(commutation(endo_nbr, exo_nbr)*dghu)...
+    dOm = kron(I_endo,ghu*Sigma_e)*(pruned_SS.commutation(endo_nbr, exo_nbr)*dghu)...
           + kron(ghu,ghu)*reshape(dSigma_e, exo_nbr^2, totparam_nbr) + kron(ghu*Sigma_e,I_endo)*dghu;
 
     % Put into tensor notation

matlab/+identification/get_perturbation_params_derivs_numerical_objective.m

@@ -95,7 +95,7 @@ if strcmp(outputflag,'dynamic_model')
         out = [Yss; g1(:); g2(:)];
     elseif options_.order == 3
         [~, g1, g2, g3] = feval([M_.fname,'.dynamic'], ys(I), exo_steady_state', M_.params, ys, 1);
-        g3 = unfold_g3(g3, length(ys(I))+M_.exo_nbr);
+        g3 = identification.unfold_g3(g3, length(ys(I))+M_.exo_nbr);
         out = [Yss; g1(:); g2(:); g3(:)];
     end
 end

matlab/+identification/numerical_objective.m

@@ -1,5 +1,5 @@
-function out = identification_numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
-% out = identification_numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
+function out = numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
+% out = numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
 % -------------------------------------------------------------------------
 % Objective function to compute numerically the Jacobians used for identification analysis
 % Previously this function was called thet2tau.m
@@ -80,7 +80,7 @@ end
 %% compute Kalman transition matrices and steady state with updated parameters
 [dr,info,M_.params] = compute_decision_rules(M_,options_,dr, steady_state, exo_steady_state, exo_det_steady_state);
 options_ = rmfield(options_,'options_ident');
-pruned = pruned_state_space_system(M_, options_, dr, indvar, nlags, useautocorr, 0);
+pruned = pruned_SS.pruned_state_space_system(M_, options_, dr, indvar, nlags, useautocorr, 0);
 
 %% out = [vech(cov(Y_t,Y_t)); vec(cov(Y_t,Y_{t-1}); ...; vec(cov(Y_t,Y_{t-nlags})] of indvar variables, in DR order. This is Iskrev (2010)'s J matrix.
 if outputflag == 1    

matlab/+identification/run.m

@@ -803,24 +803,24 @@ if iload <=0
                 iter=iter+1;
                 % note that this is not the same si_dDYNAMICnorm as computed in identification.analysis
                 % given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure
-                si_dDYNAMICnorm(iter,:) = vnorm(STO_si_dDYNAMIC(:,:,irun)./repmat(normalize_STO_DYNAMIC,1,totparam_nbr-(stderrparam_nbr+corrparam_nbr))).*normaliz1((stderrparam_nbr+corrparam_nbr)+1:end);
+                si_dDYNAMICnorm(iter,:) = identification.vnorm(STO_si_dDYNAMIC(:,:,irun)./repmat(normalize_STO_DYNAMIC,1,totparam_nbr-(stderrparam_nbr+corrparam_nbr))).*normaliz1((stderrparam_nbr+corrparam_nbr)+1:end);
                 if ~options_MC.no_identification_reducedform && ~isempty(STO_si_dREDUCEDFORM)
                     % note that this is not the same si_dREDUCEDFORMnorm as computed in identification.analysis
                     % given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure
-                    si_dREDUCEDFORMnorm(iter,:) = vnorm(STO_si_dREDUCEDFORM(:,:,irun)./repmat(normalize_STO_REDUCEDFORM,1,totparam_nbr)).*normaliz1;
+                    si_dREDUCEDFORMnorm(iter,:) = identification.vnorm(STO_si_dREDUCEDFORM(:,:,irun)./repmat(normalize_STO_REDUCEDFORM,1,totparam_nbr)).*normaliz1;
                 end
                 if ~options_MC.no_identification_moments && ~isempty(STO_si_dMOMENTS)
                     % note that this is not the same si_dMOMENTSnorm as computed in identification.analysis
                     % given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure
-                    si_dMOMENTSnorm(iter,:) = vnorm(STO_si_dMOMENTS(:,:,irun)./repmat(normalize_STO_MOMENTS,1,totparam_nbr)).*normaliz1;
+                    si_dMOMENTSnorm(iter,:) = identification.vnorm(STO_si_dMOMENTS(:,:,irun)./repmat(normalize_STO_MOMENTS,1,totparam_nbr)).*normaliz1;
                 end
                 if ~options_MC.no_identification_spectrum && ~isempty(STO_dSPECTRUM)
                     % note that this is not the same dSPECTRUMnorm as computed in identification.analysis
-                    dSPECTRUMnorm(iter,:) = vnorm(STO_dSPECTRUM(:,:,irun)); %not yet used
+                    dSPECTRUMnorm(iter,:) = identification.vnorm(STO_dSPECTRUM(:,:,irun)); %not yet used
                 end
                 if ~options_MC.no_identification_minimal && ~isempty(STO_dMINIMAL)
                     % note that this is not the same dMINIMALnorm as computed in identification.analysis
-                    dMINIMALnorm(iter,:) = vnorm(STO_dMINIMAL(:,:,irun)); %not yet used
+                    dMINIMALnorm(iter,:) = identification.vnorm(STO_dMINIMAL(:,:,irun)); %not yet used
                 end
             end
         end

matlab/+mom/objective_function.m

@@ -150,11 +150,11 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
         stderrparam_nbr = estim_params_.nvx;    % number of stderr parameters
         corrparam_nbr = estim_params_.ncx;      % number of corr parameters
         totparam_nbr = stderrparam_nbr+corrparam_nbr+modparam_nbr;
-        oo_.dr.derivs = get_perturbation_params_derivs(M_, options_mom_, estim_params_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
+        oo_.dr.derivs = identification.get_perturbation_params_derivs(M_, options_mom_, estim_params_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
         oo_.mom.model_moments_params_derivs = NaN(options_mom_.mom.mom_nbr,totparam_nbr);
-        pruned_state_space = pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 1);
+        pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 1);
     else
-        pruned_state_space = pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 0);
+        pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 0);
     end
     oo_.mom.model_moments = NaN(options_mom_.mom.mom_nbr,1);
     for jm = 1:size(M_.matched_moments,1)

matlab/+osr/objective.m

@@ -64,9 +64,9 @@ if ~options_.analytic_derivation
     loss = full(weights(:)'*vx(:));
 else
     totparam_nbr=length(i_params);
-    oo_.dr.derivs = get_perturbation_params_derivs(M_, options_, [], oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, i_params, [], [], 0); %analytic derivatives of perturbation matrices
+    oo_.dr.derivs = identification.get_perturbation_params_derivs(M_, options_, [], oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, i_params, [], [], 0); %analytic derivatives of perturbation matrices
 
-    pruned_state_space = pruned_state_space_system(M_, options_, oo_.dr, i_var, 0, 0, 1);
+    pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, oo_.dr, i_var, 0, 0, 1);
     vx = pruned_state_space.Var_y + pruned_state_space.E_y*pruned_state_space.E_y';
     dE_yy = pruned_state_space.dVar_y;
     for jp=1:length(i_params)

matlab/+pruned_SS/Q6_plication.m

@@ -49,7 +49,7 @@ for i1=1:p
             for i4=i3:p
                 for i5=i4:p
                     for i6=i5:p
-                        idx = uperm([i6 i5 i4 i3 i2 i1]);
+                        idx = pruned_SS.uperm([i6 i5 i4 i3 i2 i1]);
                         for r = 1:size(idx,1)
                             ii1 = idx(r,1); ii2= idx(r,2); ii3=idx(r,3); ii4=idx(r,4); ii5=idx(r,5); ii6=idx(r,6);
                             n = ii1 + (ii2-1)*p + (ii3-1)*p^2 + (ii4-1)*p^3  + (ii5-1)*p^4 + (ii6-1)*p^5;

matlab/+pruned_SS/pruned_state_space_system.m

@@ -418,7 +418,7 @@ if order > 1
     hx_hu    = kron(hx,hu);
     hu_hu    = kron(hu,hu);
     I_xx     = eye(x_nbr^2);
-    K_x_x    = commutation(x_nbr,x_nbr,1);
+    K_x_x    = pruned_SS.commutation(x_nbr,x_nbr,1);
     invIx_hx = (eye(x_nbr)-hx)\eye(x_nbr);
 
     %Compute unique fourth order product moments of u, i.e. unique(E[kron(kron(kron(u,u),u),u)],'stable')
@@ -596,9 +596,9 @@ if order > 1
     if order > 2
         % Some common and useful objects for order > 2
         if isempty(K_u_xx)
-            K_u_xx   = commutation(u_nbr,x_nbr^2,1);
-            K_u_ux   = commutation(u_nbr,u_nbr*x_nbr,1);
-            K_xx_x   = commutation(x_nbr^2,x_nbr);
+            K_u_xx   = pruned_SS.commutation(u_nbr,x_nbr^2,1);
+            K_u_ux   = pruned_SS.commutation(u_nbr,u_nbr*x_nbr,1);
+            K_xx_x   = pruned_SS.commutation(x_nbr^2,x_nbr);
         end
         hx_hss2  = kron(hx,1/2*hss);
         hu_hss2  = kron(hu,1/2*hss);
@@ -627,7 +627,7 @@ if order > 1
         E_xf_xfxs    = Var_z(id_z3_xf_xf, id_z2_xs   ) + E_xfxf(:)*E_xs';      %this is E[kron(xf,xf)*xs']
         E_xf_xfxf_xf = Var_z(id_z3_xf_xf, id_z3_xf_xf) + E_xfxf(:)*E_xfxf(:)'; %this is E[kron(xf,xf)*kron(xf,xf)']
         E_xrdxf = reshape(invIxx_hx_hx*vec(...
-                                             hxx*reshape( commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx'...
+                                             hxx*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx'...
                                            + hxu*kron(E_xs,E_uu)*hu'...
                                            + 1/6*hxxx*reshape(E_xf_xfxf_xf,x_nbr^3,x_nbr)*hx'...
                                            + 1/6*huuu*reshape(QPu*E_u_u_u_u,u_nbr^3,u_nbr)*hu'...
@@ -655,7 +655,7 @@ if order > 1
                 dE_xf_xfxf_xf(:,:,jp2) = dVar_z(id_z3_xf_xf , id_z3_xf_xf , jp2) + vec(dE_xfxf(:,:,jp2))*E_xfxf(:)' + E_xfxf(:)*vec(dE_xfxf(:,:,jp2))';
                 dE_xrdxf(:,:,jp2) = reshape(invIxx_hx_hx*vec(...
                     dhx(:,:,jp2)*E_xrdxf*hx' + hx*E_xrdxf*dhx(:,:,jp2)'...
-                  + dhxx(:,:,jp2)*reshape( commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx' + hxx*reshape( commutation(x_nbr^2,x_nbr,1)*vec(dE_xf_xfxs(:,:,jp2)), x_nbr^2,x_nbr)*hx' + hxx*reshape( commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*dhx(:,:,jp2)'...
+                  + dhxx(:,:,jp2)*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx' + hxx*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*vec(dE_xf_xfxs(:,:,jp2)), x_nbr^2,x_nbr)*hx' + hxx*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*dhx(:,:,jp2)'...
                   + dhxu(:,:,jp2)*kron(E_xs,E_uu)*hu' + hxu*kron(dE_xs(:,jp2),E_uu)*hu' + hxu*kron(E_xs,dE_uu(:,:,jp2))*hu' + hxu*kron(E_xs,E_uu)*dhu(:,:,jp2)'...
                   + 1/6*dhxxx(:,:,jp2)*reshape(E_xf_xfxf_xf,x_nbr^3,x_nbr)*hx' + 1/6*hxxx*reshape(dE_xf_xfxf_xf(:,:,jp2),x_nbr^3,x_nbr)*hx' + 1/6*hxxx*reshape(E_xf_xfxf_xf,x_nbr^3,x_nbr)*dhx(:,:,jp2)'...
                   + 1/6*dhuuu(:,:,jp2)*reshape(QPu*E_u_u_u_u,u_nbr^3,u_nbr)*hu' + 1/6*huuu*reshape(dE_u_u_u_u_jp2,u_nbr^3,u_nbr)*hu' + 1/6*huuu*reshape(QPu*E_u_u_u_u,u_nbr^3,u_nbr)*dhu(:,:,jp2)'...

matlab/+pruned_SS/quadruplication.m

@@ -49,7 +49,7 @@ for l=1:p
     for k=l:p
         for j=k:p
             for i=j:p                             
-                idx = uperm([i j k l]);
+                idx = pruned_SS.uperm([i j k l]);
                 for r = 1:size(idx,1)
                     ii = idx(r,1); jj= idx(r,2); kk=idx(r,3); ll=idx(r,4);
                     n = ii + (jj-1)*p + (kk-1)*p^2 + (ll-1)*p^3;                    

matlab/+pruned_SS/uperm.m

@@ -1,49 +1,49 @@
-function p = uperm(a)
-% Return all unique permutations of possibly-repeating array elements
-% =========================================================================
-% Copyright © 2014 Bruno Luong <brunoluong@yahoo.com>
-% Copyright © 2020 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-% =========================================================================
-% Original author: Bruno Luong <brunoluong@yahoo.com>, April 20, 2014
-% https://groups.google.com/d/msg/comp.soft-sys.matlab/yQKVPTYrv6Q/gw1MzNd9sYkJ
-% https://stackoverflow.com/a/42810388
-
-[u, ~, J] = unique(a);
-p = u(up(J, length(a)));
-
-function p = up(J, n)
-ktab = histc(J,1:max(J));
-l = n;
-p = zeros(1, n);
-s = 1;
-for i=1:length(ktab)
-    k = ktab(i);
-    c = nchoosek(1:l, k);
-    m = size(c,1);
-    [t, ~] = find(~p.');
-    t = reshape(t, [], s);
-    c = t(c,:)';
-    s = s*m;
-    r = repmat((1:s)',[1 k]);
-    q = accumarray([r(:) c(:)], i, [s n]);
-    p = repmat(p, [m 1]) + q;
-    l = l - k;
-end
-end
-
+function p = uperm(a)
+% Return all unique permutations of possibly-repeating array elements
+% =========================================================================
+% Copyright © 2014 Bruno Luong <brunoluong@yahoo.com>
+% Copyright © 2020 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+% =========================================================================
+% Original author: Bruno Luong <brunoluong@yahoo.com>, April 20, 2014
+% https://groups.google.com/d/msg/comp.soft-sys.matlab/yQKVPTYrv6Q/gw1MzNd9sYkJ
+% https://stackoverflow.com/a/42810388
+
+[u, ~, J] = unique(a);
+p = u(up(J, length(a)));
+
+function p = up(J, n)
+ktab = histc(J,1:max(J));
+l = n;
+p = zeros(1, n);
+s = 1;
+for i=1:length(ktab)
+    k = ktab(i);
+    c = nchoosek(1:l, k);
+    m = size(c,1);
+    [t, ~] = find(~p.');
+    t = reshape(t, [], s);
+    c = t(c,:)';
+    s = s*m;
+    r = repmat((1:s)',[1 k]);
+    q = accumarray([r(:) c(:)], i, [s n]);
+    p = repmat(p, [m 1]) + q;
+    l = l - k;
+end
+end
+
 end % uperm

matlab/cellofchar2mfile.m

@@ -1,63 +0,0 @@
-function cellofchar2mfile(fname, c, cname)
-
-% Write a cell of char in a matlab script.
-%
-% INPUTS
-% - fname [string] name of the file where c is to be saved.
-% - c     [cell]   a two dimensional cell of char.
-%
-% OUTPUTS
-% None.
-
-% Copyright © 2015-2017 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-[pathstr,name,ext] = fileparts(fname);
-
-if isempty(ext)
-    fname = [pathstr, name, '.m']
-else
-    if ~isequal(ext, '.m')
-        error(['The first argument needs to be the name of a matlab script (with an .m extension)!'])
-    end
-end
-
-if ~iscell(c)
-    error('The second input argument must be a cell!')
-end
-
-if ndims(c)>2
-    error(['The cell passed has a second argument cannot have more than two dimensions!'])
-end
-
-variablename = inputname(2);
-
-if isempty(variablename) && nargin<3
-    error(['You must pass the name of the cell (second input argument) as a string in the third input argument!'])
-end
-
-if nargin>2
-    if isvarname(cname)
-        variablename = cname;
-    else
-        error('The third input argument must be a valid variable name!')
-    end
-end
-
-fid = fopen(fname,'w');
-fprintf(fid, '%s = %s;', variablename, writecellofchar(c));
-fclose(fid);

matlab/check_model.m

@@ -2,7 +2,7 @@ function check_model(M_)
 % check_model(M_)
 % Performs various consistency checks on the model
 
-% Copyright (C) 2005-2033 Dynare Team
+% Copyright (C) 2005-2023 Dynare Team
 %
 % This file is part of Dynare.
 %

matlab/clear_persistent_variables.m

@@ -1,5 +1,5 @@
 function clear_persistent_variables(folder, writelistofroutinestobecleared)
-
+% clear_persistent_variables(folder, writelistofroutinestobecleared)
 % Clear all the functions with persistent variables in directory folder (and subdirectories).
 
 % Copyright © 2015-2019 Dynare Team
@@ -60,3 +60,51 @@ end
 
 list_of_functions_to_be_cleared;
 clear(list_of_functions{:});
+
+function cellofchar2mfile(fname, c, cname)
+% Write a cell of char in a matlab script.
+%
+% INPUTS
+% - fname [string] name of the file where c is to be saved.
+% - c     [cell]   a two dimensional cell of char.
+%
+% OUTPUTS
+% None.
+
+
+
+[pathstr,name,ext] = fileparts(fname);
+
+if isempty(ext)
+    fname = [pathstr, name, '.m'];
+else
+    if ~isequal(ext, '.m')
+        error(['The first argument needs to be the name of a matlab script (with an .m extension)!'])
+    end
+end
+
+if ~iscell(c)
+    error('The second input argument must be a cell!')
+end
+
+if ndims(c)>2
+    error(['The cell passed has a second argument cannot have more than two dimensions!'])
+end
+
+variablename = inputname(2);
+
+if isempty(variablename) && nargin<3
+    error(['You must pass the name of the cell (second input argument) as a string in the third input argument!'])
+end
+
+if nargin>2
+    if isvarname(cname)
+        variablename = cname;
+    else
+        error('The third input argument must be a valid variable name!')
+    end
+end
+
+fid = fopen(fname,'w');
+fprintf(fid, '%s = %s;', variablename, writecellofchar(c));
+fclose(fid);

matlab/compute_model_moments.m

@@ -1,32 +0,0 @@
-function moments=compute_model_moments(dr,M_,options_)
-%
-% INPUTS
-%    dr:        structure describing model solution
-%    M_:   structure of Dynare options
-%     options_
-%
-% OUTPUTS
-%    moments: a cell array containing requested moments
-%
-% SPECIAL REQUIREMENTS
-%    none
-
-% Copyright © 2008-2017 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-[ivar,vartan,options_] = get_variables_list(options_,M_);
-moments = th_autocovariances(dr,ivar,M_,options_,options_.nodecomposition);

matlab/convergence_diagnostics/mcmc_ifac.m

@@ -69,3 +69,27 @@ for i=(Nc/2)+1: Nc+1
 end
 Parzen=Parzen';
 Ifac= 1+2*sum(Parzen(:).* AcorrXSIM);
+
+
+function acf = dyn_autocorr(y, ar)
+% function acf = dyn_autocorr(y, ar)
+% autocorrelation function of y
+%
+% INPUTS
+%   y:       time series
+%   ar:      # of lags
+%
+% OUTPUTS
+%   acf:       autocorrelation for lags 1 to ar
+%
+% SPECIAL REQUIREMENTS
+%   none
+
+y=y(:);
+acf = NaN(ar+1,1);
+acf(1)=1;
+m = mean(y);
+sd = std(y,1);
+for i=1:ar
+    acf(i+1) = (y(i+1:end)-m)'*(y(1:end-i)-m)./((size(y,1))*sd^2);
+end

matlab/dyn_autocorr.m

@@ -1,40 +0,0 @@
-function acf = dyn_autocorr(y, ar)
-% function acf = dyn_autocorr(y, ar)
-% autocorrelation function of y
-%
-% INPUTS
-%   y:       time series
-%   ar:      # of lags
-%
-% OUTPUTS
-%   acf:       autocorrelation for lags 1 to ar
-%
-% SPECIAL REQUIREMENTS
-%   none
-
-% Copyright © 2015-16 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-
-y=y(:);
-acf = NaN(ar+1,1);
-acf(1)=1;
-m = mean(y);
-sd = std(y,1);
-for i=1:ar
-    acf(i+1) = (y(i+1:end)-m)'*(y(1:end-i)-m)./((size(y,1))*sd^2);
-end

matlab/dyn_diag_vech.m

@@ -1,31 +0,0 @@
-function d = dyn_diag_vech(Vector)
-% This function returns the diagonal elements of a symmetric matrix
-% stored in vech form
-%
-% INPUTS
-%   Vector             [double]   a m*1 vector.
-%
-% OUTPUTS
-%   d                  [double]   a n*1 vector, where n solves n*(n+1)/2=m.
-
-% Copyright © 2010-2017 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-m = length(Vector);
-n = (sqrt(1+8*m)-1)/2;
-k = cumsum(1:n);
-d = Vector(k);

matlab/dynare_config.m

@@ -47,6 +47,7 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
      '/AIM/' ; ...
      '/backward/' ; ...
      '/cli/' ; ...
+     '/conditional_forecasts/'; ...
      '/convergence_diagnostics/' ; ...
      '/discretionary_policy/' ; ...
      '/distributions/' ; ...
@@ -57,19 +58,24 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
      '/gsa/' ; ...
      '/kalman/' ; ...
      '/kalman/likelihood' ; ...
+     '/latex/' ; ...
      '/lmmcp/' ; ...
      '/modules/dseries/src/' ; ...
      '/reporting/' ; ...
+     '/matrix_solver/'; ...
      '/moments/'; ...
      '/ms-sbvar/' ; ...
      '/ms-sbvar/identification/' ; ...
      '/nonlinear-filters/' ; ...
      '/ols/' ; ...
+     '/optimal_policy/' ; ...
      '/optimization/' ; ...
      '/pac-tools/' ; ...
      '/parallel/' ; ...
      '/partial_information/' ; ...
      '/perfect-foresight-models/' ; ...
+     '/shock_decomposition/' ; ...
+     '/stochastic_solver/' ; ...
      '/utilities/dataset/' ; ...
      '/utilities/doc/' ; ...
      '/utilities/estimation/' ; ...

matlab/dynare_gradient.m

@@ -1,66 +0,0 @@
-function [F,G] = dynare_gradient(fcn,x,epsilon,varargin)
-% Computes the gradient of a function from R^m in R^n.
-%
-% INPUTS:
-%  fcn      [string]  name of the matlab's function.
-%  x        [double]  m*1 vector (where the gradient is evaluated).
-%  epsilon  [double]  scalar or m*1 vector of steps.
-%
-% OUTPUTS:
-%  F        [double]  n*1 vector, evaluation of the function at x.
-%  G        [double]  n*m matrix, evaluation of the gradient at x.
-%
-% OUTPUTS
-%
-% Copyright © 2010-2017 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-% Evaluate the function at x.
-F = feval(fcn, x, varargin{:});
-
-% (G)Set dimensions.
-m = length(x);
-n = length(F);
-
-% Initialization of the gradient.
-G = NaN(length(F),length(x));
-
-if length(epsilon==1)
-    H = epsilon*eye(m);
-else
-    H = diag(epsilon);
-end
-
-% Compute the gradient.
-for i=1:m
-    if size(x,1)>size(x,2)
-        h = H(i,:);
-    else
-        h = H(:,i);
-    end
-    [Fh,~,~,flag] = feval(fcn, x+transpose(h), varargin{:});
-    if flag
-        G(:,i) = (Fh-F)/epsilon;
-    else
-        [Fh,~,~,flag] = feval(fcn, x-transpose(h), varargin{:});
-        if flag
-            G(:,i) = (F-Fh)/epsilon;
-        else
-            error('-- Bad gradient --')
-        end
-    end
-end

matlab/estimation/dsge_likelihood.m

@@ -482,14 +482,14 @@ if analytic_derivation
         old_analytic_derivation_mode = options_.analytic_derivation_mode;
         options_.analytic_derivation_mode = kron_flag;
         if full_Hess
-            DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], true);
+            DERIVS = identification.get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], true);
             indD2T = reshape(1:M_.endo_nbr^2, M_.endo_nbr, M_.endo_nbr);
             indD2Om = dyn_unvech(1:M_.endo_nbr*(M_.endo_nbr+1)/2);
             D2T = DERIVS.d2KalmanA(indD2T(iv,iv),:);
             D2Om = DERIVS.d2Om(dyn_vech(indD2Om(iv,iv)),:);
             D2Yss = DERIVS.d2Yss(iv,:,:);
         else
-            DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], false);
+            DERIVS = identification.get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], false);
         end
         DT = zeros(M_.endo_nbr, M_.endo_nbr, size(DERIVS.dghx,3));
         DT(:,M_.nstatic+(1:M_.nspred),:) = DERIVS.dghx;

matlab/estimation/dsge_simulated_theoretical_covariance.m

@@ -132,7 +132,7 @@ for file = 1:NumberOfDrawsFiles
         if ~options_.pruning
             tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
         else
-            pruned_state_space = pruned_state_space_system(M_, options_, dr, obs_var, options_.ar, 1, 0);
+            pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, dr, obs_var, options_.ar, 1, 0);
             tmp{1} = pruned_state_space.Var_y;            
             for i=1:nar
                 tmp{i+1} = pruned_state_space.Corr_yi(:,:,i);                

matlab/evaluate_dynamic_model.m

@@ -1,29 +0,0 @@
-function residuals = evaluate_dynamic_model(dynamicmodel, endogenousvariables, exogenousvariables, params, steadystate, leadlagincidence, samplesize)
-
-% Copyright © 2016 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-ny = length(steadystate);
-periods = rows(exogenousvariables);
-
-residuals = zeros(ny,samplesize);
-icols = find(leadlagincidence');
-
-for t = 2:samplesize+1
-    residuals(:,t-1) = dynamicmodel(endogenousvariables(icols), exogenousvariables, params, steadystate, t);
-    icols = icols + ny;
-end

matlab/moments/disp_th_moments_pruned_state_space.m

@@ -52,7 +52,7 @@ for i=1:nvars
     obs_var(i,1) = find(strcmp(M_.endo_names(i_var(i),:), M_.endo_names(dr.order_var)));
 end
 
-pruned_state_space = pruned_state_space_system(M_, options_, dr, obs_var, options_.ar, 1, 0);
+pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, dr, obs_var, options_.ar, 1, 0);
 
 
 m = pruned_state_space.E_y;

matlab/my_subplot.m

@@ -1,56 +0,0 @@
-function my_subplot(i,imax,irow,icol,fig_title)
-
-% function my_subplot(i,imax,irow,icol,fig_title)
-% spreads subplots on several figures according to a maximum number of
-% subplots per figure
-%
-% INPUTS
-%   i:          subplot number
-%   imax:       total number of subplots
-%   irow:       maximum number of rows in a figure
-%   icol:       maximum number of columns in a figure
-%   fig_title:  title to be repeated on each figure
-%
-% OUTPUT
-%   none
-%
-% SPECIAL REQUIREMENTS
-%   none
-
-% Copyright © 2003-2009 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-nfig_max = irow*icol;
-if imax < nfig_max
-    icol = ceil(sqrt(imax));
-    irow=icol;
-    if (icol-1)*(icol-2) >= imax
-        irow = icol-2;
-        icol = icol-1;
-    elseif (icol)*(icol-2) >= imax
-        irow = icol-2;
-    elseif icol*(icol-1) >= imax
-        irow = icol-1;
-    end
-end
-
-i1 = mod(i-1,nfig_max);
-if i1 == 0
-    figure('Name',fig_title);
-end
-
-subplot(irow,icol,i1+1);

matlab/save_results.m

@@ -1,38 +0,0 @@
-function save_results(x,s_name,names)
-
-% function save_results(x,s_name,names)
-% save results in appropriate structure
-%
-% INPUT
-%   x: matrix to be saved column by column
-%   s_name: name of the structure where to save the results
-%   names: names of the individual series
-%
-% OUTPUT
-%   none
-%
-% SPECIAL REQUIREMENT
-%   none
-
-% Copyright © 2006-2009 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-global oo_
-
-for i=1:size(x,2)
-    eval([s_name deblank(names(i,:)) '= x(:,i);']);
-end